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Asian Business Research Journal 
Vol. 10, No. 4, 7-19, 2025 
ISSN: 2576-6759 
DOI: 10.55220/25766759.353 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

  

 
Determinants of Budget Implementation in Nigeria 

 
Joseph Seun KOLAWOLE1  
Adeyemi Wasiu ALABI2 
Gbenga Ayodele FALANA3 
 

 
 

1,3Department of Accounting, College of Social and Management Science, Afe Babalola University, Ekiti State, 
Nigeria. 
Email: kolawolejs@pg.abuad.edu.ng  
Email: falanaga@pg.abuad.edu.ng    
2Department of Accounting, Babcock Business School, Babcock University, Ilishan-Remo, Ogun State, Nigeria. 
Email: alabi0696@babcock.edu.ng  
( Corresponding Author) 

 
 

 
Abstract 

The below-par performance of budget implementation (BI) in Nigeria has contributed to the 
country’s economic instability and public distrust in fiscal policies. This study primarily 
investigates the determinants of BI in Nigeria by focusing on foreign exchange rates (FEXR), 
inflation rates (INFR), interest rates (INTR), and corruption control (CINR). Using annual data 
from 2008 to 2024, this study employs time series analysis to examine the causal relationships 
between key macroeconomic variables and BI. Findings reveal that FEXR significantly influences 
BI, underscoring Nigeria’s fiscal vulnerability to FEXR fluctuations. INTR also exhibits a 
significant impact on BI, highlighting the importance of INTR policies in BI. Conversely, INFR 
has no direct effect on BI, suggesting that inflation’s influence on fiscal performance is secondary. 
The study concludes that Nigeria’s BI is usually undermined and disrupted by FEXR and INTR 
instability. The study recommends that the Nigerian government prioritize FEXR stability 
through sound monetary policies to mitigate currency-related fiscal risks. Furthermore, the 
Central Bank of Nigeria should implement measures to maintain favorable INTR, ensuring that 
borrowing costs do not hinder BI. Strengthening institutional accountability is essential to 
minimizing corruption’s negative effects on fiscal performance. These findings provide valuable 
insights for policymakers seeking to enhance budget efficiency and promote sustainable economic 
development in Nigeria. 

 
Keywords: Budget implementation, Corruption index, Foreign exchange rates, Inflation rates, Interest rates. 

 
1. Introduction 

The consistent failure of African governments to implement budgets effectively continues to erode public 
confidence in governmental institutions. Citizens become disillusioned with the government's ability to manage 
public resources, which has led to apathy and reduced civic engagement. There may be a time in the future when 
citizens will rise in agitation against the governments in Africa due to public distrust in fiscal policies (Bredino et 
al., 2022). Nwala and Ogboji (2020) opined that discrepancies between planned and actual expenditures can result 
in stalled projects and underutilized investments, thereby limiting economic progress. This highlights that 
ineffective budget implementation often leads to suboptimal allocation and utilization of resources, hindering 
economic growth. 

Sedgo and Omgba (2023) argue that ineffective budget implementation in Africa fosters corruption and 
financial mismanagement. The misallocation of capital budgets due to weak oversight and governance structures 
creates loopholes for embezzlement and inefficiencies. This leads to poor service delivery, as public funds meant for 
infrastructure and essential services are either mismanaged or diverted. Supporting studies by Ibeh (2025) and 
Matovu (2025) reinforce this view, highlighting that without strong financial controls and transparency, budgetary 
weaknesses will continue to hinder sustainable growth in Africa. 

Nigeria continues to face challenges in aligning budgetary projections with actual implementation. The 
Ministry of Finance (2023) reported that capital budget implementation rates averaged less than 65% between 
2018 and 2022, reflecting administrative bottlenecks and revenue mobilization challenges. Nigeria has persistently 
grappled with poor budget implementation and economic instability due to underlying macroeconomic and 
institutional constraints (Olawale et al., 2023). These challenges have been worsened by corruption, which 
undermines fiscal discipline and reduces the effectiveness of policy measures designed to manage debt and 
implement budgets. This underscores why Nigeria struggles with economic instability and ineffective public 
financial management, limiting the country’s ability to achieve sustainable development. 

Effective execution of budgetary plans is essential for achieving long-term development goals and ensuring 
that government spending aligns with developmental priorities (Fatukasi et al., 2020). However, despite successive 

mailto:kolawolejs@pg.abuad.edu.ng
mailto:falanaga@pg.abuad.edu.ng
mailto:alabi0696@babcock.edu.ng
https://doi.org/10.55220/25766759.353


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reforms, Nigeria continues to face obstacles such as delayed project execution, cost overruns, and inadequate 
resource allocation, compounded by external economic shocks and structural inefficiencies (Ministry of Finance, 
2023; Debt Management Office, 2023). While budget implementation is an important aspect of fiscal management, 
understanding the determinants of budget implementation is crucial for designing effective fiscal policies. 
Identifying key factors such as foreign exchange stability, inflation control, improved revenue generation, and 
institutional accountability will empower policymakers to address these challenges more strategically (Michael et 
al., 2020). 

Existing research primarily focuses on the effects of individual macroeconomic variables on fiscal performance 
(Okafor et al., 2021; Ugwuanyi et al., 2021; Onoh et al., 2021; Onwuka, 2020; Onyebuchukwu, 2019), often 
neglecting their combined impact in the context of budget implementation. Additionally, few analyses examine how 
foreign exchange rates, inflation rates, and interest rates collectively shape budget implementation, leaving a 
significant gap in understanding the interplay of these variables in the Nigerian context (Agboola & Ojo, 2021; Obi, 
2020; Charles & Chilaka, 2019). Lastly, studies on Nigeria’s fiscal performance seldom integrate corruption indices 
as critical variable influencing outcomes. 

To address these gaps, this study primarily investigates the determinants of budget implementation in Nigeria, 
using foreign exchange rates, inflation rates, and interest rates as proxies while incorporating the corruption index 
as a control variable. Specifically, the study seeks to examine the extent to which foreign exchange rate fluctuations 
influence budget implementation in Nigeria, assess the impact of the inflation rate on Nigeria’s fiscal performance, 
evaluate how interest rates affect budget execution, and analyze the moderating role of corruption in the 
relationship between macroeconomic variables and fiscal outcomes. 

The study focuses on Nigeria's fiscal performance from 2008 to 2023, leveraging annual data on foreign 
exchange rates, inflation rates, interest rates, and corruption indices. It examines the dynamic interactions between 
these variables and their combined influence on budget implementation. Given Nigeria’s growing fiscal challenges, 
this study is timely and significant. Understanding the determinants of budget implementation provides valuable 
insights for policymakers to design targeted interventions. By incorporating corruption as a control variable, the 
study highlights the importance of institutional quality in fiscal management. Moreover, the findings will 
contribute to existing literature by offering a comprehensive analysis of macroeconomic and institutional factors 
influencing Nigeria’s fiscal performance, thereby guiding future policy decisions to enhance budget efficiency. 
 

2. A Review of Extant Literature 
This section extensively examines the theories and empirical studies that explain the concept related to the 

determinants of budget implementation and debt servicing. 
 

2.1. Conceptual Review 
2.1.1. Budget Implementation 

Budget implementation in the view of Alabi et al. (2024), involve the detailed approach or procedural steps 
undertaken to ensure that a planned budget is well executed. This involves key activities such as revenue 
mobilization, efficient expenditure management, and monitoring to ensure compliance with budgetary goals while 
making necessary adjustments to address economic changes. Effective budget implementation depends on 
institutional capacity, fiscal discipline, macroeconomic stability, and governance quality, which help align spending 
with developmental priorities (Schick, 2022; IMF, 2023). However, efficient budget execution fosters economic 
growth by directing public funds into productive sectors, reducing unemployment, and preventing fiscal deficits 
(Okonjo et al., 2023). It also enhances fiscal discipline by minimizing overrun, thereby strengthening accountability 
and resource management. Transparent execution builds public trust in government institutions, while poor 
implementation fosters corruption and weakens governance (World Bank, 2023; Transparency International, 
2023). 

Recent studies on budget implementation in developing nations highlight challenges and opportunities in 
improving fiscal performance. In Nigeria, Okeke and Adewuyi (2023) found that only 60% of approved capital 
expenditures between 2015 and 2022 were executed due to revenue shortfalls and bureaucratic inefficiencies, 
underscoring the need for stronger institutional capacity. Transparency International (2023) further demonstrated 
that nations with lower corruption indices achieve significantly higher implementation rates, reinforcing the 
detrimental impact of corruption on fiscal performance. Conversely, Kenya’s adoption of digital tools and enhanced 
monitoring mechanisms increased budget implementation rates by 25% over five years, showcasing the 
transformative role of technology (Mwangi et al., 2023). An IMF (2023) analysis linked higher budget 
implementation rates to stronger GDP growth, as efficient spending supports timely investments in critical 
sectors. Meanwhile, the World Bank (2023) identified wage bill pressures and resource misallocation as major 
obstacles in South Africa, recommending fiscal discipline and strategic reallocations. These studies collectively 
emphasize the importance of governance, technology, and institutional reforms in achieving effective budget 
implementation and fostering economic growth. 
 

2.1.2. Foreign Exchange Rate 
Kwan and Kanda (2024) describe foreign exchange rate (FX rate) as the value at which one currency can be 

exchanged for another and plays a crucial role in international trade, investment, and economic policy. It is 
influenced by multiple factors, including inflation, interest rates, political stability, and market speculation (Obi, 
2020). The FX rate has significant economic implications, as a depreciating currency can make exports cheaper and 
imports more expensive, potentially improving the trade balance, while an appreciating currency may lead to trade 
deficits (Ahmed et al., 2024). Exchange rate fluctuations also impact inflation, with weaker currencies raising 
import prices and contributing to inflation, particularly in import-dependent nations. Additionally, currency 
movements affect capital flows, as investors prefer stable environments for higher returns, making volatile 
currencies less attractive (Ubadike et al., 2023). 



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Exchange rate fluctuations can significantly influence budget implementation by affecting revenue generation, 
and expenditure planning. When exchange rate volatility negatively impacts economic growth, as noted by 
Bamidele (2024), government revenues may decline due to reduced business activity, lower tax collection, and 
unstable foreign investment flows. In commodity-dependent economies, external shocks from exchange rate 
instability can lead to unpredictable revenue streams, making budget planning more challenging. As opined  by 
Alabi et al. (2025), an increase in the exchange rate of US dollar to the Nigerian Naira implies that the Naira has 
been devalued consequent to the increase in the exchange rate, thereby reducing the purchasing power of the 
Nigerian Naira. In view of this, Afolabi (2023) highlight that currency devaluation can trigger inflationary 
pressures, which increase the cost of public sector expenditures, including wages, infrastructure projects, and social 
programs. Inflation-driven budget overruns can force governments to either cut essential services or seek 
additional financing, potentially leading to fiscal deficits. 
 

2.1.3. Inflation Rate 
Inflation represents a sustained increase in the overall price level of goods and services in an economy, 

resulting in diminished purchasing power as consumers can buy less with the same amount of money (Ana et al., 
2024). This implies that governments may struggle to finance planned projects as the actual cost of goods and 
services rises beyond initial estimates. Typically measured by indices like the Consumer Price Index (CPI), 
inflation impacts multiple facets of the economy (Ha et al., 2024). It erodes purchasing power, particularly affecting 
low-income households and reduces the real returns on savings and fixed-income investments. Businesses face 
challenges in long-term planning due to cost and pricing uncertainties, while fixed-income earners, such as retirees, 
bear the brunt of income redistribution. Moreover, inflation can spark a wage-price spiral, where rising wages lead 
to higher production costs, perpetuating further price increases. For governments, this creates fiscal instability, as 
revenue projections may fall short in real terms while expenditure requirements increase. Fixed-income earners, 
such as public sector workers and pensioners, face declining purchasing power, leading to pressures for wage 
adjustments that further strain budget sustainability. 

Bill et al. (2024) and Jongrim et al. (2023) explained that between 2021 and 2023, the global economy 
experienced an unprecedented surge in inflation, driven by intertwined factors such as COVID-19-induced supply 
chain disruptions, expansive fiscal and monetary policies, and geopolitical tensions like the Russia-Ukraine conflict. 
These factors contributed to rising costs of essential commodities, making it difficult for governments to 
implement budgets effectively without frequent revisions. Additionally, aggressive interest rate hikes aimed at 
curbing inflation increased borrowing costs, limiting governments' ability to finance deficits or invest in capital 
projects without incurring higher debt burdens. While Ha et al. (2025) pointed out that inflation moderation efforts 
helped some economies approach stability, consumer prices remain elevated, complicating budgetary planning. 
Governments must balance inflation control measures with the need to maintain economic growth and public 
welfare spending. In some cases, achieving price stability may require deflationary measures, which could slow 
economic activity and further challenge budget execution. 
 

2.1.4. Interest Rate 
Interest rates, representing the cost of borrowing or return on savings, play a crucial role in monetary policy 

by influencing economic activities such as consumption, investment, and inflation. These dynamics directly impact 
budget implementation, as government expenditures and revenues are sensitive to interest rate fluctuations. Lower 
rates encourage borrowing and spending, fostering growth, while higher rates curb excessive demand and control 
inflation (Mishkin, 2021). Central banks adjust interest rates to maintain economic stability, tailoring policies to 
inflationary or deflationary conditions (Bernanke, 2023). However, frequent rate adjustments can create budgetary 
uncertainties, making it difficult for governments to plan long-term fiscal policies effectively. The effects of interest 
rates extend beyond domestic economies; higher rates attract foreign capital, strengthening local currencies but 
potentially reducing export competitiveness (IMF, 2023). Additionally, rising rates increase public debt servicing 
costs, impacting fiscal sustainability (OECD, 2023). As such, interest rates shape financial and macroeconomic 
landscapes, influencing savings behavior, investment trends, and global trade flows (Blanchard et al., 2022). 

Between 2021 and 2023, global interest rates underwent significant fluctuations as economies navigated post-
pandemic recovery and inflationary pressures. Initially, central banks kept rates low to support growth, allowing 
governments to finance stimulus packages. However, surging inflation led to aggressive hikes by 2022, 
complicating budget implementation. For instance, the U.S. Federal Reserve raised its benchmark rate from near 
zero to over 5%, reducing inflation but increasing borrowing costs for government spending programs (Federal 
Reserve, 2023). Similarly, the European Central Bank’s rate hikes lowered inflation but constrained fiscal flexibility 
(European Central Bank (ECB), 2023) Developing economies, such as Nigeria, faced inflation exceeding 20% in 
2023 despite rate increases, limiting the effectiveness of monetary policy in supporting budget execution (CBN, 
2023). Higher inflation eroded the real value of government revenues, making it harder to fund budgeted 
expenditures. Japan, in contrast, maintained ultra-low rates to counter deflation, but this approach limited the 
government's ability to stimulate the economy through monetary policy alone (Bank of Japan (BOJ), 2023). The 
rate hikes also affected financial markets, raising borrowing costs, reducing corporate profits, and slowing housing 
markets (IMF, 2023). These effects, in turn, influenced tax revenues, debt sustainability, and public expenditure 
planning. 
 

2.1.5. Corruption Index Rate 
The Corruption Perceptions Index (CPI), developed by Transparency International in 1995, ranks countries 

annually based on perceived public sector corruption using data from credible sources like the World Bank and 
World Economic Forum (Transparency International, 2023). The CPI scores range from 0 (highly corrupt) to 100 
(very clean), reflecting expert and business opinions on corruption prevalence. High corruption levels indicate 
systemic governance issues, undermining judicial integrity, diverting resources, and eroding public trust—factors 
that directly impact budget implementation. Effective budget execution depends on transparent allocation and 



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prudent resource utilization. However, corruption weakens institutional oversight, leading to misallocation, 
embezzlement, and inflated contracts that derail government spending efficiency. The 2023 CPI highlights 
persistent global challenges, with only 28 out of 180 countries improving over 12 years, while 34 have significantly 
declined (EQS Group, 2023). In Africa, corruption remains widespread, diverting essential resources meant for 
public services and infrastructure projects, thereby undermining fiscal discipline and development efforts 
(Transparency International, 2023; World Bank, 2023). 
 

2.2. Theoretical Framework  
2.2.1. Institutional Theory 

Institutional theory formed the bedrock of this study. Institutional theory, rooted in Max Weber's sociological 
principles and expanded by Douglass North (1990), emphasizes the role of both formal and informal institutions—
such as laws, norms, and conventions—in shaping economic and organizational behaviour (Douglass, 1990). The 
theory posits that institutions provide a framework for social, economic, and political interactions, significantly 
influencing decisions and outcomes (Agarwal et al., 2024). Key assumptions include the dual impact of formal and 
informal structures, the persistence of historical contexts (path dependency), and the necessity for organizations 
and governments to align with institutional frameworks to gain legitimacy (Davis & Boianovsky, 2023). These 
principles underscore the role of institutions in guiding governance and policy actions (North, 1990). 

The theory is particularly relevant to economic processes including budget implementation. Strong institutions 
enhance fiscal performance by promoting transparency, accountability, and efficient resource allocation, while weak 
institutions—marked by corruption and governance failures—undermine economic stability. Recent empirical 
studies validate these claims. For example, Adesina and Adeyemo (2022) show that governance quality 
significantly impacts budget execution in Sub-Saharan Africa, while Ahmed et al. (2022) highlight the importance 
of institutional strength for effective inflation management. These findings demonstrate the critical influence of 
institutional quality on fostering fiscal stability and economic development. 

Recent studies continue to emphasize the importance of institutional quality in shaping fiscal and 
macroeconomic outcomes, especially in developing economies. Adesina and Okafor (2023) found that weak 
institutions, characterized by corruption and poor regulatory frameworks, negatively affect budget execution and 
debt servicing in Sub-Saharan Africa. Similarly, Ahmed et al. (2022) showed that strong institutional structures 
facilitate effective monetary policy implementation, which stabilizes inflation and enhances fiscal sustainability. The 
IMF (2023) highlighted that institutional reforms, such as improving central bank autonomy, help reduce 
exchange rate volatility, thereby supporting fiscal planning and debt management. Together, these studies confirm 
that strengthening institutions is essential for addressing fiscal and economic challenges in developing countries 
like Nigeria. 

Institutional theory offers a valuable framework for understanding the relationship between macroeconomic 
variables—such as foreign exchange rates, inflation, and interest rates—and fiscal outcomes, including budget 
implementation (North, 1990; IMF, 2023). Effective institutions ensure currency stability through central bank 
autonomy and transparent policies, reducing exchange rate volatility and its fiscal implications (Adesina & Okafor, 
2023). Robust monetary policies driven by strong institutions help control inflation, preventing disruptions in 
budget planning and minimizing debt servicing costs (Ahmed et al., 2022; World Bank, 2023). Additionally, strong 
governance lowers borrowing risks, reducing interest rates and alleviating fiscal strain (IMF, 2023). However, 
institutional theory has been critiqued for its static framework, overemphasis on formal structures, and limited 
focus on micro-level dynamics (Adesina & Okafor, 2023). Despite these criticisms, its relevance to macroeconomic 
determinants underscores the importance of institutional reforms in promoting fiscal sustainability and economic 
growth (North, 1990; World Bank, 2023). 
 

2.3. Empirical Review 
2.3.1. Foreign Exchange Rate and Budget Implementation  

The World Bank (2023) employed a mixed-method approach, combining macroeconomic data from 50 
developing economies with qualitative case studies, and applied Fixed Effects Regression models to examine 
budget implementation in sub-Sahara Africa. The study found that overvalued exchange rates inflated fiscal deficits 
by raising the costs of imports and external borrowings, thereby complicating budget execution. Conversely, 
undervalued exchange rates disrupted infrastructure projects due to reduced purchasing power for capital imports. 
Exchange rate reforms, particularly market-determined rates, were identified as essential for achieving fiscal 
stability and improving budget outcomes. 

The OECD (2023) applied cross-sectional analysis and Structural Equation Modeling (SEM) to assess 
exchange rate impacts among its member countries. Exchange rate volatility was found to disrupt budget 
implementation by inflating costs for foreign-denominated debt and procurement. Stable exchange rates, supported 
by coordinated fiscal and monetary policies, enhanced debt sustainability and fiscal planning. The study 
emphasized aligning fiscal frameworks with exchange rate policies to strengthen budget execution and long-term 
economic stability. 

Ahmed et al. (2022) analyzed data from 2010 to 2021 using regression analysis and Granger causality tests to 
assess the impact of exchange rate fluctuations on Nigeria’s fiscal performance. The study found that naira 
depreciation led to a 25% increase in debt servicing costs between 2020 and 2022, resulting in significant 
budgetary shortfalls. The study emphasized the need to diversify government revenue sources and strengthen 
foreign exchange reserves to mitigate fiscal vulnerabilities and ensure sustainable economic management. 

Collectively, the reviewed studies highlight the critical role of exchange rate stability in fiscal sustainability 
and budget implementation. Globally, foreign exchange volatility disrupts budget execution, and hampers 
infrastructure projects. This study, therefore, hypothesizes that: 

H01: Foreign exchange rate has no significant influence on budget implementation in Nigeria. 
 
 



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2.3.2. Inflation Rate and Budget Implementation  
The IMF (2023) conducted a time-series analysis using panel data econometrics, focusing on 50 emerging 

markets from 1990 to 2022 to evaluate macroeconomic stability in the face of inflationary shocks with insights 
from emerging markets. Data on inflation rates and budgetary performance reports were sourced from the IMF 
World Economic Outlook and country-specific financial records. The study found that inflation volatility disrupted 
fiscal planning, leading to frequent budget revisions and implementation delays. The findings emphasized the 
importance of effective monetary policy and inflation targeting to stabilize fiscal outcomes. 

The World Bank (2023) employed a comparative analysis methodology to assess inflation’s impact on fiscal 
stability in 25 low-income and 30 middle-income countries between 2000 and 2022. Data included budget 
implementation reports, inflation trends, and debt profiles from the World Bank database and national statistics 
agencies. Persistent inflation was found to undermine budget execution by increasing government procurement 
and capital project costs. Countries with inflation exceeding 10% experienced, on average. 

Studying inflation and fiscal management, the OECD (2022) used panel regression analysis to study 37 
member countries from 1995 to 2021. Data on inflation rates and budget deficit statistics were obtained from the 
OECD database. The study revealed that inflation exacerbates fiscal strain by raising borrowing costs and 
reducing the real value of government revenue. Stable inflation rates below 2% supported efficient budget 
execution. Coordinated fiscal and monetary policies were identified as effective measures for enhancing resilience 
against inflationary shocks and improving fiscal performance. 

Adler and Schwartz (2023) conducted an econometric analysis on interest rate volatility in Latin using the 
generalized method of moments (GMM) on 45 developing economies from 1990 to 2022. Data on annual inflation 
rates and government expenditure reports were sourced from the World Development Indicators. Findings 
indicated that inflation above 12% significantly impaired budget implementation, particularly for infrastructure 
projects. Adesina and Bello (2023) used descriptive and inferential analysis with ordinary least squares (OLS) 
regression to examine Nigeria’s annual fiscal performance from 2000 to 2022. Data on inflation rates, government 
budget execution reports, and debt servicing costs were obtained from the Central Bank of Nigeria (CBN) and the 
Debt Management Office (DMO). The study found that inflation exceeding 15% increased the cost of capital 
projects by over 25%, disrupting budget execution.  

Studying inflation and fiscal sustainability in Nigeria, Ahmed et al. (2022) performed panel data analysis 
incorporating structural breaks, focusing on fiscal data from Nigeria’s federal and state governments between 2000 
and 2021. Data on inflation trends and public expenditure reports were collected from the Nigerian Bureau of 
Statistics (NBS) and the CBN. Persistent double-digit inflation caused significant budgetary shortfalls and delayed 
project implementation. However, global and Nigerian empirical studies consistently show high inflation rates 
adversely impact budget implementation. Globally, inflation raises procurement costs, and disrupts fiscal planning, 
and budget execution. This study, therefore, hypothesizes as follows: 

H02: Inflation rate has no significant influence on budget implementation in Nigeria. 
 

2.3.3. Interest Rate and Budget Implementation  
The IMF (2023) conducted a quantitative analysis using panel data regression to examine the impact of rising 

global interest rates on fiscal stability in 50 emerging markets. The study analyzed annual data from 2000 to 2022, 
focusing on interest rates and budgetary allocations. It was found that higher global interest rates significantly 
increase debt servicing costs, particularly for countries reliant on variable-rate external debt. This rise in costs 
reduces fiscal space for budget implementation. However, countries with stronger fiscal frameworks and diversified 
borrowing portfolios experienced lower fiscal disruptions, emphasizing the importance of sound fiscal management 
practices. 

Using structural equation modeling (SEM), the World Bank (2023) analyzed the causal relationship between 
interest rates, fiscal performance, and institutional factors in 40 low- and middle-income countries. The study used 
data from 2005 to 2022, including interest rate trends, debt profiles, and fiscal expenditures. The findings revealed 
that a 1% increase in interest rates leads to a 5% rise in debt servicing costs. High interest rates discourage public 
borrowing for development projects, resulting in under-executed budgets. However, countries with strong 
institutional quality were better equipped to mitigate interest rate risks and maintain fiscal stability. 

Adler and Jin (2022) employed econometric modeling with time series data from 10 Latin American countries 
between 1995 and 2021 to examine interest rate volatility in Latin America. The study analyzed central bank reports 
on interest rates and government budgets. The results concluded that interest rate volatility increases fiscal 
unpredictability, complicating budget planning. Countries with a higher proportion of fixed-rate debt were less 
affected by interest rate fluctuations. Furthermore, transparent monetary policies were shown to reduce borrowing 
costs and improve budget execution, demonstrating the critical role of monetary policy in mitigating interest rate 
risks. 

The OECD (2022) used a mixed-methods approach, combining quantitative analysis and case studies, to 
examine the impact of interest rate fluctuations on fiscal performance in 20 member countries from 2000 to 2020. 
The study found that low-interest rate environments improved budget implementation by reducing debt servicing 
costs, allowing more resources to be allocated to public investments. Conversely, prolonged periods of high interest 
rates strained fiscal budgets and curtailed developmental expenditures. Efficient debt management systems were 
highlighted as essential for enhancing fiscal performance in fluctuating interest rate environments. 

Adesina and Okafor (2023) applied autoregressive distributed lag (ARDL) models to analyze Nigeria’s federal 
budgetary data from 1990 to 2022 in a study on the impact of interest rates on Nigeria’s fiscal budget. The study 
focused on interest rates and budget implementation rates. It was found that high interest rates significantly 
increased Nigeria’s domestic debt servicing costs, reducing funds available for capital projects. Interest rate hikes in 
the domestic bond market discouraged private-sector lending, further straining fiscal budgets. 

Ahmed et al. (2022) used vector error correction models (VECM) to analyze Nigeria’s fiscal and monetary data 
from 2000 to 2021. The study revealed that rising interest rates, both domestic and international, resulted in a 30% 
increase in debt servicing costs over five years. Budget implementation was constrained by high borrowing costs, 



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delaying critical infrastructure projects. The researchers emphasized the importance of strengthening institutional 
mechanisms for debt management and improving coordination between monetary and fiscal policies to mitigate the 
adverse effects of rising interest rates on Nigeria’s fiscal performance. Therefore, this study hypothesizes that: 

H03: Interest rate has no significant influence on budget implementation in Nigeria. 
 

2.4. Conceptual Framework 
The conceptual framework depicted in Figure 1 demonstrates the relationships among factors influencing 

budget implementation in Nigeria. 
 

 
Figure 1. The Conceptual Framework. 

 

3. Methodology 
This study involves time series analysis; therefore, both ex-post facto and longitudinal research methods were 

used. The study covered the period from 2008 to 2024 fiscal year. The Federal Government of Nigeria (FGN) 
served as the study focus and population. Secondary data was obtained from the FGN's Annual Budget and reports 
from the CBN, Debt Management Office (DMO), and the Federal Ministry of Finance. Data was subjected to 
multiple regression analysis, and the hypotheses were tested using the Stata statistical package. 
The study’s econometric model regressed budget implementation against the foreign exchange, inflation, and 
interest rates, with the corruption index as a control variable. The regression equations are stated as follows: 

BI𝑡= β0 + β1FEXR𝑡+ β2INFR𝑡+ β3INTR𝑡+ β4CINR𝑡+ 𝜀𝑡                (1) 

Where: 
BI= Budget Implementation 
FEXR= Foreign Exchange Rate 
INFR= Inflation Rate 
INTR= Interest Rate 
CINR= Corruption Index Rate 

β0= Constant 

β1-β4= Gradients 

𝑡= time 

𝜀= Error Term 
 

3.1. Measurement and Description of Variables 
The variables being studied, together with their description, measurement, data source and supporting 

literature, are shown in Table 1. 
 

Table 1. Measurement and Description of Research Variables. 

SN Variable Description Measurement Data Source Literature 
Evidence 

1 Budget 
Implementation 

Budget implementation entails 
executing the government's 
financial strategy, ensuring that 
spending and revenues line with 
the approved budget 

the ratio of the 
public sector 
expenditure to its 
planned or 
approved budget 

Government 
Annual 
Reports. 

(DMO, 2022) 

2 Foreign Exchange 
Rate 

The foreign exchange rate is the 
value of one country's currency in 
relation to another's currency. 

Official exchange 
rate at the end of 
the year 

CBN 
REPORT 

Ubadike et al., 
2023 

3 Inflation Rate The inflation rate refers to the rate 
at which prices rise over time. 

Official inflation 
rate at the end of 
the year 

CBN 
REPORT 

Ana et al., 2024 

4 Interest Rate The interest rate is the percentage 
charged on the entire amount of a 
loan or paid on deposits over a 
given period. 

Official interest 
rate at the end of 
the year 

CBN 
REPORT 

Bernanke, 2023 

5 Corruption Index 
Rate 

The Corruption Perceptions Index 
(CPI) ranks countries on a scale of 
0 (very corrupt) to 100 (extremely 
clean). 

perceived levels of 
public sector 
corruption, scoring 
on a scale of 0 
(highly corrupt) to 

CPI 2023 
Reports 

Transparency 
International, 
2023 



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100 (very clean). 

 

4. Data Analysis and Discussion of Findings  
This section depicts the characteristics of the variables used, data analysis, and study findings. 

  

4.1. Descriptive Statistics 
Statistical features for each variable, along with information on their distribution and properties across 

datasets, are included in Table 2. BI (Budget implementation) has a mean of 1.237 indicating that real spending 
exceeds the anticipated revenue. BI value ranges from 0.456 to 2.446, revealing significant variation in budget 
execution efficiency. The standard deviation is 0.50, indicating a moderate variability in budget execution. Also, the 
skewness is slightly right-skewed at 0.67, while the kurtosis is 3.23 indicating a close-to-normal distribution. 
Likewise, the foreign exchange rate (FEXR) statistics show substantial variability, with a mean of 340.30 and a 
huge standard deviation of 340.49. This implies a highly volatile or depreciating currency era. The lowest value is 
approximately 118.57, while the highest value is 1550.70. The skewness of 2.85 implies a highly right-skewed. This 
indicates currency depreciation. The kurtosis of 10.69 is high. This means the presence of extreme exchange rate 
fluctuations. 

In the same vein, the sample's inflation rate (INFR) ranges from roughly 8.05% to 34.8%, with an average of 
14.74%. The substantial dispersion and high average indicate that inflation is a major concern and a possible risk 
factor influencing budget implementation. The standard deviation of 6.62 implies substantial inflation volatility. 
The skewness of 1.82 indicates a right-skewed, with higher extreme values. The kurtosis of 6.12 implies a fat-tailed 
with periods of very high inflation. Additionally, the interest rate (INTR) statistics show a minimum of 6% and a 
maximum of 27.5%, a mean value of 13.12%. This variation suggests that borrowing terms and risk premiums 
varied during the observations. The skewness of 1.35 indicates a right skew. This implies periods of high interest 
rates. The kurtosis of 5.84 connotes a fat-tailed distribution. This suggests extreme interest rate variations. On the 
other hand, with a mean of 25.65 on a scale that ranges from 0 to 100 basis points, this index evaluates perceived 
corruption in the public sector. The narrow range (24 to 28) and somewhat low standard deviation of 1.32 imply 
that corruption levels are reasonably stable within the sample. The skewness of 0.18 indicates close to symmetric 
distribution while the kurtosis of 1.70 implies a slightly platykurtic, with few extreme values. 

Finally, it can be inferred that the FEXR is extremely volatile, with sharp movements and a substantial right 
skewness, indicating periods of rapid currency depreciation. However, BI has a generally stable distribution, which 
means that government expenditure is continuously implemented with minor deviations. The corruption Index 
(CINR) is generally steady, but its low mean value indicates considerable concerns about corruption. 
 

Table 2. Descriptive Statistics. 

Variable Obs. Mean Std. Dev. Min. Max. Skewness Kurtosis 

BI 17 1.24 0.50 0.46 2.45 0.67 3.23 

FEXR 17 340.30 340.49 118.57 1550.70 2.85 10.69 

INFR 17 14.74 6.62 8.05 34.80 1.82 6.12 

INTR 17 13.12 4.86 6.00 27.50 1.35 5.84 

CINR 17 25.65 1.32 24.00 28.00 0.18 1.70 
Note: Results of mean, median, minimum and maximum of each variable from analysis of study data are shown above table. 

 

4.2. Test of Variables  
This section includes essential pre- and post-estimation tests to ensure the study's conclusions are relevant and 

reliable. 
 

4.2.1. Pre-estimation Test 
To ensure that the assumptions of the selected model were fulfilled and that there was enough data for analysis, 

the following tests were carried out. 
  

4.2.1.1. Variables’ Stationary Test 
Table 3 displays the results of the stationary test. The Dickey-Fuller (ADF) tests the presence of a unit root in 

the variables under study. According to the null hypothesis, the distribution of data varies over time, but in the 
alternative hypothesis, the distribution of data remains constant across time. If the significant result is greater than 
0.05, the null hypothesis is accepted; if it is less than 0.05, it is rejected. All variables have p-values greater than 
0.05, indicating that variables are non-stationary. Accordingly, log transformation was carried out among the 
variables, the p-values of BI, FEXR, INFR, INTR, and CINR were 0.0017, 0.0000, 0.0392, 0.0072, and 0.0000, 
respectively. This implies that all variables were stationary. 

Despite this, variables were checked for cointegration using the Engle-Granger Cointegration Test. As shown 
in Table 3, the test statistics for the model was -4.209 with a p-value of 0.0006. In this regard, the null hypothesis is 
rejected. This indicates that the cointegration regression's residuals are stationary. The study shows cointegration 
with stationary residuals, indicating a long-term equilibrium relationship between variables, despite individual 
variables being stationary. Also, selection order criteria were determined using the Akaike Information Criterion 
and Schwarz-Bayesian Criterion. The result for SBIC (Schwarz-Bayesian Criterion) was -417.838 at lag 4 while the 
result for Akaike Criterion (AIC) was -421.227 at lag 4. The study chooses Lag 4 as the optimal lag selection since 
it has the lowest value. 
 
 
 
 



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Table 3. Stationary Test. 

Variable Dickey-Fuller Lag (0) Dickey-Fuller Lag (0) After Transformation 

  Z-value P-value Z-value P-value 

BI -0.87 0.7969 -3.955 0.0017 

FEXR 4.63 1.0000 -5.972 0.0000 

INFR 1.68 0.9981 -2.956 0.0392 

INTR 0.63 0.9884 -3.531 0.0072 

CINR -2.55 0.1036 -5.513 0.0000 

Engle-Granger Cointegration Test -4.209 0.0006   
Akaike Information Criterion  -417.838    
Schwarz-Bayesian Criterion -421.227       

 
Table 3 shows the estimated coefficients of different stationary tests of the study’s variables 
 
4.2.1.2. Multicollinearity 

Variance inflation factor (VIF) analysis is shown in Table 4. FEXR, INFR, INTR and CINR have 1.27, 1.24, 
1.29 and 1.29, respectively as VIF. These imply that there exists no multicollinearity. The mean VIF factor is 1.35, 
indicating the absence of multicollinearity among the variables. 
 

Table 4. Variance Inflation Factor. 

Variable VIF 1/VIF 

CINR 1.61 0.621874 

INFR 1.29 0.773040 

FEXR 1.27 0.786158 

INTR 1.24 0.808915 

Mean VIF 1.35  
Note: Results of a test of multicollinearity relationships amongst variables of the study are shown in the above table. 

 

4.2.1.3. Correlation Analysis  
Table 5 presents pairwise correlation coefficients and test results for independent variables. The test results 

revealed the correlation between the FEXR and INFR is 0.2067 (p = 0.4260), indicating an insignificant but weak 
relationship. This shows that there is little correlation between these variables. Also, the relationship between 
FEXR and INTR is 0.0329 (p = 0.9003). This implies an insignificant but weak positive connection. The 
correlation coefficient between INFR and INTR is 0.0234 (p = 0.9290), indicating an insignificant relationship. 
There are no significant correlations between CINR and other variables. The study concludes that there is no 
multicollinearity among variables. 
 

Table 5. Pairwise Correlation. 

  FEXR INFR INTR CINR 

FEXR 1.0000    
INFR 0.2067 1.0000   

 0.4260    
INTR 0.0329 0.0234 1.0000  

 0.9003 0.9290   
CINR -0.2815 0.3246 0.3790 1.0000 

  0.2738 0.2037 0.1335  
Note: Results of a pairwise correlation coefficient test of relationships amongst variables of the study are shown in the above table.  

 

4.2.2. Post-Estimation Tests 
In Table 6, a heteroskedasticity test was used to determine the constant variance of residuals or changes in 

fitted values. The test results for the model revealed a chi-square of 0.77 and a significant value of 0.3813, showing 
high evidence of homoskedasticity in the residuals of the study's model.  

Similarly from Table 6, Durbin’s Alternative Test for Autocorrelation was used to determine autocorrelation in 
the data distribution. The Chi-square value of 1.069 and p-value of 0.3012>0.05 for the model implies that the 
study accepts the null hypothesis at the 5% significance level. The study, therefore, concludes that the residuals did 
not exhibit considerable autocorrelation, indicating that errors are not correlated over time. 

In the same vein, the Skewness/Kurtosis tests for normality were used to determine whether the variables 
were normal. The result as contained in Table 6 showed that FEXR, INFR, INTR, and CINR were normally 
distributed while BI was not normally distributed. 
 

Table 6. Summary of Post-Estimation Test Results. 

Test F-Statistic P-value 

Heteroskedasticity test  0.77 0.3813 
Durbin’s Alternative Test for Autocorrelation  1.069 0.3012 
Skewness/Kurtosis tests for normality:    
FEXR 0.71 0.7009 
INFR 0.41 0.8144 
INTR 4.03 0.1335 
CINR 3.06 0.2169 



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BI 9.85 0.0073 

 
Table 6 shows the estimated coefficients of f-statistics and p-value for the model. 
 

4.3. Vector Error Correction Model (VECM) 
Vector Error Correction Model (VECM) was used to analyse short- and long-term relationships identified by 

the Engle-Granger Cointegration Test. R-square and p-value are used to illustrate the short-run dynamics in the 
model. As presented in Table 7, FEXR has an R-square of 0.9799 and a p-value of 0.0000, suggesting that the 
model explains short-term changes and provides significant evidence for short-run effects. This implies that FEXR 
is highly responsive to economic shocks. The R-square for INFR is 0.9344, with p-values of 0.0047. This implies 
that 93.4% of short-term inflation changes are accounted for. The R-square for INTR is 0.9929, with a p-value of 
0.0000, indicates that the model explains 99% of the variability. This suggests that interest rates respond quickly to 
economic changes. The CINR has a p-value of 0.0000 and an R-square of 0.9710, which indicates a very high 
explanatory power. 
 
                                                  Table 7. Vector Error-Correction Model. 

Sample: 2011 - 2024 Number of Obs. = 14 

  AIC  = -121.71 
Log-likelihood =   915.9416 HQIC  = -121.98 
Det (Sigma_ml) =-1.02e-63 SBIC  = -118.79 
Equation  Parms       RMSE R-sq chi2 P>chi2 
D_FEXR           12 0.0621 0.9799 97.3367 0.0000 
D_INFR              12 0.0913 0.9344 28.5056 0.0047 
D_INTR             12 0.0321 0.9929 280.781 0.0000 
D_CINR           12 0.0183 0.971 67.0094 0.0000 

 
Table 7 shows the estimated coefficients, R square-values, and p-values of the study’s main variables and 

control variables for model. 
 

4.3.1. Cointegrating Equations in VECM 
The cointegrating equation illustrates the long-term equilibrium relationship between the variables. As 

shown in Table 8, the chi-square statistic is 125.4675 and the p-value is 0.0000<0.05. The result shows that the 
cointegrating equation is statistically significant, indicating a strong long-term relationship among the variables in 
the model. 
 

Table 8. Cointegrating Equations in VECM. 

Equation Parameters (Parms) Chi² (Wald Statistic) P-value (P>Chi²) 

Cointegrating Equation 3 125.4675 0.0000 

 
Table 8 shows the estimated coefficients of Cointegrating Equations in VECM. 

 
4.3.2. Stability Test 

The roots of the companion matrix plot are used to assess the stability of a vector error correction model 
(VECM). It assists in detecting structural breaks or changes in the relationship between variables. Figure 2 
illustrates the companion matrix plot. The VECM model is stable, with all roots within or on the unit circle, and 
no eigenvalues outside the circle. This indicates no divergence over time. This stability allows for confident 
impulse response functions, variance decomposition, and forecasting, ensuring well-behaved relationships among 
variables for accurate forecasting. 
 

 
Figure 2. Companion Matrix. 

Source: Researchers’ Computation (2025). 

 

4.3.3. Impulse Response Function (IRF) Plot 
The Impulse Response Function (IRF) plot illustrates how a shock to one variable affects another over time. 

Figure 3 shows the impulse response function (IRF) plot. The x-axis (step) depicts the periods following the shock. 
The y-axis shows the size of the response. The blue line illustrates the cumulative impulse response function, which 
shows how the response accumulates over time. The grey shaded area represents the 95% confidence interval (CI), 
which indicates the range of uncertainty surrounding the response. Each panel depicts the response of budget 
implementation (BI) to shocks in different variables. 

-1
-.5

0
.5

1
Im

ag
ina

ry

-1 -.5 0 .5 1
Real

Roots of the companion matrix



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Also, BI's response to CINR is close to zero, indicating a shock to corruption that has little to no impact on 
budget implementation (BI). The confidence intervals are narrow, suggesting a stable estimate. BI's response to 
FEXR is positive, implying that a shock to FEXR increases BI over time. The response becomes stronger around 
steps 5-10, indicating a delayed but growing effect. Again, BI's response to INFR is positive, meaning that an 
increase in inflation positively affects budget implementation. The response strengthens over time, stabilizing 
around steps 7-10. BI's response to INTR shows a small positive response initially, but it starts declining after step 
5. The response fluctuates slightly but remains within a stable confidence range. 
 

 
Figure 3. Impulse Response Function. 

Source: Researchers’ Computation (2025) 

 

4.4. Granger Causality Tests 
Granger causality tests assess if historical data series on one variable aids in the prediction of another. Granger 

causality is shown by a low p-value (usually less than 0.05), which results in the rejection of the null hypothesis. 
The Granger causality test results are presented in Table 9. 
 

4.4.1. Foreign Exchange Rate and Its Influence on Budget Implementation.  
From Table 9, the relationship between BI and FEXR is denoted by a Chi-statistic of 14.8690 and a p-value of 

0.0010 < 0.05. This suggests statistically significant but strong evidence of causality. Therefore, the Foreign 
Exchange Rate significantly influences budget implementation. This result agrees with the findings of Adler and 
Garcia-Macia (2022), the World Bank (2023), OCED (2023), and IMF (2023). 
 

4.4.2. Inflation Rate and Its Influence on Budget Implementation 
As shown in Table 9, BI and INFR have a Chi-square of 2.6314 and a p-value of 0.2680 > 0.05. This 

suggests that INFR does not Granger-cause BI. Inflation Rate has no significant influence on Budget 
Implementation. While this agrees with the null hypothesis, this result however disagrees with the findings of 
Adesina and Bello (2023), the World Bank (2023), OCED (2023), and IMF (2023). 
 

4.4.3. Interest Rate and Its Influence on Budget Implementation 
Lastly from Table 9, BI and INTR have a Chi-square of 7.7533 and a p-value of 0.0210 < 0.05. This suggests 

that INFR influences BI. The inflation rate has a major impact on budget implementation. This is not consistent 
with the null hypothesis, it also does not contradict the conclusions of Ahmed et al. (2023) and Adesina and Bello 
(2023). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

-.5

0

.5

1

1.5

-.5

0

.5

1

1.5

0 5 10 0 5 10

irf_model, BR, CINR irf_model, BR, FEXR

irf_model, BR, INFR irf_model, BR, INTR

95% CI cumulative irf

step

Graphs by irfname, impulse variable, and response variable



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Table 9. Granger Causality Wald tests 

Equation Excluded chi2 df Prob > chi2 

BI FEXR 14.8690 2 0.0010 
BI INFR 2.6314 2 0.2680 
BI INTR 7.7533 2 0.0210 
BI CINR 26.9530 2 0.0000 
BI ALL 47.5930 8 0.0000 
FEXR BI 6.5276 2 0.0380 
FEXR INFR 1.6510 2 0.4380 
FEXR INTR 3.1452 2 0.2080 
FEXR CINR 2.5313 2 0.2820 
FEXR ALL 16.8980 8 0.0310 
INFR BI 19.2490 2 0.0000 
INFR FEXR 14.5780 2 0.0010 
INFR INTR 20.7920 2 0.0000 
INFR CINR 17.4470 2 0.0000 
INFR ALL 78.7390 8 0.0000 
INTR BI 2.8280 2 0.2430 
INTR FEXR 4.6585 2 0.0970 
INTR INFR 7.4070 2 0.0250 
INTR CINR 3.5245 2 0.1720 
INTR ALL 10.5940 8 0.2260 

CINR BI 11.725 2 0.0030 
CINR FEXR 0.1051 2 0.9490 
CINR INFR 0.4738 2 0.7890 
CINR INTR 35.293 2 0.0000 
CINR ALL 96.489 8 0.0000 

 
Table 9 shows the estimated coefficients of Granger Causality Wald test results of all variables of the study. 

 

4.5.  Discussion of Findings 
The findings of the Granger causality test provide useful insights into the relationships between key economic 

and fiscal variables in Nigeria. Foreign exchange rates and budget implementation have a substantial bidirectional 
causal relationship. This indicates that foreign exchange rates and budget implementation are inextricably related, 
with foreign exchange rates influencing how the government executes its budget and vice versa. While inflation 
has no significant effect on budget implementation, budget implementation has a substantial influence on inflation. 
This implies that government budgeting can influence inflation. Also, interest rates have a significant impact on 
budget implementation, but budget implementation does not affect interest rates. Therefore, interest rates are a 
major factor in deciding how the government distributes and uses its budget. In general, the findings emphasize 
that foreign exchange rates and interest rates have the most significant influence on Nigeria's budget 
implementation while inflation has a less direct influence.  
 

5. Conclusion and Recommendations 
This study investigated the determining factor of budget implementation in Nigeria. The literature on these 

factors was evaluated. Annual data on the foreign exchange rate, inflation rate, interest rate, corruption perception 
index, and budget implementation ratio were gathered from CBN annual reports between 2008 and 2024. These 
data were examined using times series analysis. The study demonstrates that budget implementations are 
significantly affected by foreign exchange rates. This emphasises how susceptible Nigeria's fiscal stability is to 
changes in the foreign exchange rate. Also, interest rates have a complementary causal relationship with budget 
implementation. This highlights the importance of interest rate policies in determining government spending. 
Inflation rates have little or no effect on budget implementation. This suggests that inflation has a more indirect 
and secondary effect on budget implementation. 

Based on this, the following recommendations were made: Firstly, the government should prioritize exchange 
rate stability through good monetary policy. This will reduce the risk of currency changes affecting budget 
implementation, hence preserving fiscal stability. Secondly, since interest rates have significant effects on budget 
implementation, the Central Bank of Nigeria (CBN) should try to keep interest rates low to boost the economy. 
The effects of interest rate fluctuations on government spending and fiscal capacity should be closely monitored by 
policymakers. 
 

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